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Abstract #0999

A Fully Convolutional Neural Network for 3D Volumetric Liver Lesion Segmentation

Sean Sall1, Anitha Krishnan1, Jesse Lieman-Sifry1, Felix Lau1, Matthieu Le1, Matt DiDonato1, Albert Hsiao2, Claude Sirlin2, John Axerio-Cilies1, and Daniel Golden1

1Arterys, San Francisco, CA, United States, 2Radiology, UC San Diego Health, La Jolla, CA, United States

We present an automated approach to liver lesion segmentation in abdominal MRI scans. We use a 3D fully convolutional neural network to segment liver lesions; segmentations are then used to estimate longest linear diameter (LLD) and volume. We show that the median LLD error is 2.01 mm and that these estimates are within limits of clinically usability as part of a semi or fully-automated workflow. Automating lesion segmentation may pave the way for tracking lesion volume and tumor burden as well as treatment response.

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